Overview

Classify breast masses from four clinical features using Kumo Tabular and the UCI Mammographic Mass dataset. The notebook runs the small model on CPU, uses labeled examples as context, and returns class probabilities for held-out rows. It compares prediction accuracy and ROC AUC with a majority-class baseline.

A GPU is not required. The notebook installs its dependencies and downloads the dataset and pretrained weights, so you need internet access. Use a separate notebook environment for its pinned dependencies.

• Model: Kumo Tabular
• Library: Structured Data Models
• Example: Open the Kumo Tabular CPU notebook guide
• Benchmarks: Tabular evaluation results and limitations

Predict churn labels for customer rows from a single table. The Kumo Tabular quickstart uses labeled rows with age and spend features as context, builds a classification request for new rows, and returns predicted labels and class probabilities. Before running, install the client and prepare a compatible Kumo Tabular NIM endpoint.

The quickstart uses tiny illustrative tables to demonstrate the API. It does not evaluate real-world churn prediction quality.

• Model: Kumo Tabular
• Library: Structured Data Models Client
• Setup: Connect to a Kumo Tabular NIM
• Example: Run the Kumo Tabular classification quickstart

Use purchase history and the relationships between customers, orders, and products to suggest what a customer may buy next. The AdventureWorks notebook requests the top 10 products a customer is likely to order in the next 30 days. You will learn how to express a recommendation task in a predictive query and read the ranked product IDs and scores returned by Kumo Relational. Before running, install the client and prepare a compatible NIM endpoint and the AdventureWorks data files.

• Model: Kumo Relational
• Setup: Connect Structured Data Models Client to NIM
• Example: Open the AdventureWorks notebook and follow “Recommend products.”

Predict future values from a history of timestamped measurements. The Kumo Forecast example reads an ETTh sample CSV and predicts the next 100 values of its LULL signal. Install the Forecast environment and provide the CSV files expected by the script, or update the paths and columns for your own data. Context-enhanced forecasting requires a separate history CSV. Kumo Forecast is intended for research and development only.

• Model: Kumo Forecast
• Library: Kumo-TS Forecast setup and guide
• Example: Open the forecasting code

Find readings that depart from the usual patterns across a set of sensors. The Kumo Anomaly example generates synthetic sensor data with spikes, dips, shifts, and noise, then returns anomaly flags and error scores. Install the Anomaly environment and allow model-weight downloads or supply local weights. Confirm that inference produced the Anomaly flags and MAE scores; a dataset preview alone does not confirm prediction. Kumo Anomaly is intended for research and development only.

• Model: Kumo Anomaly
• Library: Kumo-TS Anomaly setup and guide
• Example: Open the anomaly-detection code

Build a model for a prediction task you define on your own relational data. This Kumo Engine guide uses AdventureWorks to predict whether active customers will order again in the next 90 days. You will prepare the tables and graph, train and evaluate the model, then save it with its training metrics and predictions for later use. Before running, configure the documented GPU environment and writable local scratch, then download and prepare the AdventureWorks data.

• Tool: Kumo Engine
• Training: Define and train a task-specific model
• Guide and code: Predict a yes or no outcome on Linux